{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "66cbbaf8",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c01c19dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Dict, List, Union\n",
    "import numpy as np\n",
    "from langchain_ollama import OllamaEmbeddings\n",
    "from beir.datasets.data_loader import GenericDataLoader\n",
    "from beir.retrieval.search.dense import DenseRetrievalExactSearch\n",
    "from beir.retrieval.evaluation import EvaluateRetrieval\n",
    "from beir import util\n",
    "import json\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac011c1c",
   "metadata": {},
   "source": [
    "# Utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b83e7900",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BEIROllamaEmbeddings:\n",
    "    \"\"\"\n",
    "    Adapter that makes LangChain's OllamaEmbeddings compatible with BEIR.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        base_url: str,\n",
    "        model: str,\n",
    "        batch_size: int = 64,\n",
    "    ) -> None:\n",
    "        self.batch_size = batch_size\n",
    "        self.embeddings = OllamaEmbeddings(\n",
    "            base_url=base_url,\n",
    "            model=model,\n",
    "        )\n",
    "\n",
    "    def _batch_embed(self, texts: List[str]) -> np.ndarray:\n",
    "        vectors = []\n",
    "\n",
    "        for i in range(0, len(texts), self.batch_size):\n",
    "            batch = texts[i : i + self.batch_size]\n",
    "            batch_vectors = self.embeddings.embed_documents(batch)\n",
    "            vectors.extend(batch_vectors)\n",
    "\n",
    "        return np.asarray(vectors, dtype=np.float32)\n",
    "\n",
    "    def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        BEIR query encoder\n",
    "        \"\"\"\n",
    "        return self._batch_embed(queries)\n",
    "\n",
    "    def encode_corpus(\n",
    "        self,\n",
    "        corpus: Union[List[Dict[str, str]], Dict[str, Dict[str, str]]],\n",
    "        **kwargs,\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        BEIR corpus encoder\n",
    "        \"\"\"\n",
    "        if isinstance(corpus, dict):\n",
    "            corpus = list(corpus.values())\n",
    "\n",
    "        texts = []\n",
    "        for doc in corpus:\n",
    "            title = (doc.get(\"title\") or \"\").strip()\n",
    "            text = (doc.get(\"text\") or \"\").strip()\n",
    "\n",
    "            if title:\n",
    "                texts.append(f\"{title}\\n{text}\")\n",
    "            else:\n",
    "                texts.append(text)\n",
    "\n",
    "        return self._batch_embed(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af3eb66d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_hf_to_beir(hf_dataset):\n",
    "    corpus, queries, qrels = {}, {}, {}\n",
    "    \n",
    "    for i, data in enumerate(hf_dataset):\n",
    "        docid = f\"doc_{i}\"\n",
    "        queryid = f\"q_{i}\"\n",
    "        \n",
    "        # El código es el documento (lo que el agente debe recuperar)\n",
    "        corpus[docid] = {\"title\": data.get(\"func_name\", \"\"), \"text\": data['code']}\n",
    "        \n",
    "        # El docstring es la consulta (lo que el usuario pide)\n",
    "        queries[queryid] = data['docstring']\n",
    "        \n",
    "        # Relación 1 a 1: la query i busca el código i\n",
    "        qrels[queryid] = {docid: 1}\n",
    "            \n",
    "    return corpus, queries, qrels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9528fb6",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "230aae25",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_dataset = load_dataset(\"google/code_x_glue_tc_nl_code_search_adv\", split=\"test\")\n",
    "corpus, queries, qrels = convert_hf_to_beir(raw_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13050d31",
   "metadata": {},
   "source": [
    "# Test qwen3-0.6B-emb:latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514540af",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BEIROllamaEmbeddings(\n",
    "    base_url=\"http://localhost:11434\",\n",
    "    model=\"qwen3-0.6B-emb:latest\",\n",
    "    batch_size=64,\n",
    ")\n",
    "\n",
    "# Inicializar buscador y evaluador\n",
    "retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
    "evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
    "\n",
    "# Ejecutar recuperación\n",
    "results = evaluator.retrieve(corpus, queries)\n",
    "\n",
    "# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
    "ndcg, _map, recall, precision = evaluator.evaluate(\n",
    "    qrels, results, [1, 3, 5, 10]\n",
    ")\n",
    "\n",
    "print(f\"Resultados para CodeXGLUE:\")\n",
    "print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
    "print(\"Recall@10:\", recall[\"Recall@10\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e643ca",
   "metadata": {},
   "source": [
    "# Test qwen2.5:1.5b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ced1c25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NDCG: {'NDCG@1': 0.02333, 'NDCG@3': 0.03498, 'NDCG@5': 0.0404, 'NDCG@10': 0.04619, 'NDCG@100': 0.07768}\n",
      "MAP: {'MAP@1': 0.02083, 'MAP@3': 0.03083, 'MAP@5': 0.03375, 'MAP@10': 0.03632, 'MAP@100': 0.04123}\n",
      "Recall: {'Recall@1': 0.02083, 'Recall@3': 0.04417, 'Recall@5': 0.0575, 'Recall@10': 0.07417, 'Recall@100': 0.23144}\n",
      "Precision: {'P@1': 0.02333, 'P@3': 0.01556, 'P@5': 0.01267, 'P@10': 0.00833, 'P@100': 0.00277}\n"
     ]
    }
   ],
   "source": [
    "model = BEIROllamaEmbeddings(\n",
    "    base_url=\"http://localhost:11434\",\n",
    "    model=\"qwen2.5:1.5b\",\n",
    "    batch_size=64,\n",
    ")\n",
    "\n",
    "# Inicializar buscador y evaluador\n",
    "retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
    "evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
    "\n",
    "# Ejecutar recuperación\n",
    "results = evaluator.retrieve(corpus, queries)\n",
    "\n",
    "# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
    "ndcg, _map, recall, precision = evaluator.evaluate(\n",
    "    qrels, results, [1, 3, 5, 10]\n",
    ")\n",
    "\n",
    "print(f\"Resultados para CodeXGLUE:\")\n",
    "print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
    "print(\"Recall@10:\", recall[\"Recall@10\"])"
   ]
  }
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